Customer interactions are precious moments for the company where an impact can be achieved. The use of call centers has become a key channel for managing corporate-to-customer relations. A call center allows for both 24×7 service and support, as well as for selling and marketing of the corporate products. Call centers have become an industry, which relies on hardware (switch boxes, computer-telephone interfaces) as well as software for managing the interactions with customers (CRM software). Since a major portion of the interactions between the company and its customers are carried out by the call center, analytical capabilities have been used to generate marketing and sale support services for the corporate agents. “Best offer” and “campaign management” modules, which use results of analytical models (some times commonly referred to as “data mining engines”), are commonly deployed. These engines are often used as static “recommendations”. In the absence of real-time (that is, during the call, i.e., the discussion with the customer) data capabilities to manage interactions, and with lack of capability to modify and guide the agent's conversation scripts, there is no demand for immediate, tight loop improvement of these capabilities.
Currently available analytical customer relation management (CRM) systems rely on pre-defined analysis of trends as well as of prior knowledge of each particular customer. Data-Mining engines analyze historical customer data, transactions and profiles, in order to generate statistical predictions as to customer preferences, needs, and expected responses. The more detailed the user profile and the customer history, and the larger the customer base, the more accurate the predictions can be. Nevertheless, almost always the operational CRM systems operate in an incomplete knowledge environment, as the profile of the customer interacting with the agent is incomplete. An additional aspect of these analytical tools (i.e., the data mining engines) is that as the computation of the analysis of the data-mining engines is time-consuming, these engines are typically initiated only once a period; during this initiation, the engines typically compute a static prediction for each customer in the customer database. This is demonstrated in FIG. 4, showing the prior art. New customers or changes during the period since the last data mining run, cannot be taken into account. Furthermore, for any new customer, a simulation has to be generated in order to determine his profile. Further, there is no pro-active attempt to gather more information during a current conversation of the agent with the customer in order to refine the prediction for that specific customer, according to the information missing from the customer's profile. Furthermore, as data mining is applied to the information that is enclosed within the CRM system, the models produced typically contain only information that is included in the system.
The model in the context of this application is a prediction mechanism: for example, given some key parameters of the customer, the model may return the likelihood that this customer will buy each of the company products. It should be noted herein that throughout this application, the term “products” refers also to services. Models contain information (that is, are used to predict) about customer likelihood to buy each specific product, about customer categorization, and about market trends.
Models can be implemented in two forms: as a set of tables, where one can look up an entry—and find the requested, predictive value, or as a program—which includes a user and interface. Upon entering the relevant parameters, and based on the model, the program returns the predictive values.
Data-Mining engines are based on statistical methods (mostly regressions). They use large volume data sets in order to form a statistical model. Typically these engines are used for creating association rules, categorization and clustering of populations, and for attribute selection. Data mining engines have been typically used for:    A. Targeted population access, in campaign management or even simply in best matching of products to customers;    B. Customer preservation purposes, based on specific customer profiles;    C. Anticipating marketing responses and loads;    D. Supporting “best-offer” activities, for example, cross-sell and up-sell activities within the call center interactions.
Typical prior art CRM environments are defined to reflect a fixed process. Accordingly, there is no pro-active attempt to gather more information in order to refine the prediction for each customer, based on data-mining needs, as information missing from the specific customer profile cannot be easily integrated into the predefined CRM processes. Further, as data mining is applied to the information that is enclosed within the CRM system, the models typically refer only to the information that is included and gathered by the predefined CRM system. Accordingly, information about specific calls, unstructured information, and additional information (e.g. agent related information, timing of processes) cannot be easily included in the model. Also, information that is not recorded in the CRM (for example, the customer refusal for an offer) cannot be used for future analysis of marketing trends.
All these usages can be applied statically by using the off-line capabilities of the Data-Mining engines. However, it has been found that a more accurate and continuous improvement can be obtained by tightly integrating the data mining model and the operational data with the run-time environment.
For the purpose of explaining the advantages of the present invention, let's examine a typical call center of an insurance company. Typically, insurance companies set the price of a car insurance using customer specific parameters. These parameters include information about the car, the drivers and their driving history, and the vehicle usage. In a typical case, there are many parameters of the drivers which may have an impact on the probability for a claim. Obviously, some of these parameters are well known, (e.g., the age, gender, driving experience, previous driving violations, and previous claims), and therefore it has become a de-facto standard in the insurance industry to use them while determining the policy price. Imagine, however, that in order to achieve competitive advantage, the marketing group within the insurance company wishes to better qualify customer profiles. For example, the marketing group may assume that there are several additional parameters, for example, P1 . . . Pk, that can more effectively predict the customer value. The company then faces two challenges: the first one is to rapidly modify the existing CRM environment and to build the business process to collect P1 . . . Pk. The second challenge is that once the mechanism for collecting, storing and analyzing this information is in place for new customers, the challenge is then how to gather this additional information also for the existing customers. It may require building special processes for completing the missing information.
It is vital to overcome this challenge for existing customers, as such an update of the additional parameters values P1 . . . Pk for each existing customer would better predict the response of said customer for an up-sell offer. However, since said additional information is missing, the model that can be applied is only the old partial model.
The call center generally costs a huge amount of money to the company. The time which each agent spends with the customer is precious. If, however, the agent can sell to the customer one extra product, this may significantly increase the effectiveness of that agent, and will increase the company revenues. However, there is a problem that the agent faces: there are many parameters about the client that are available to the company (assuming that this is an existing client), and there are many more parameters that are unknown (particularly when the client is a new client). So, based on known specific customer parameters, the data mining engine may predict which of the company products has the highest probability that the client will buy. However, what if the current customer data lacks one vital parameter and therefore the model prediction cannot be accurate or cannot be generated at all? In such a case, there is first a need for the company to determine the most vital unknown parameter for the calling customer. Only then is it possible to select the “best product offer”. The determination of the most vital parameter is important, as generally the client is impatient, and the call time is expensive. For a new customer who calls the first time, this is even a higher challenge, as there are many more missing parameters, some of which are not included in the standard, predefined CRM process.
In one aspect of the invention, the present invention guides the agent during the discussion with the customer to ask the customer said vital question, in order to determine, from the client, the missing parameter which is most vital to the company. Moreover, based on prior analysis, this question may depend on many parameters of the present call. For example, if a customer has called to discuss about product A of the company (which he has purchased before), it is important to know whether product B may be of interest for the customer. However, this may depend on the customer's age, in other cases on family status, in other cases, on the customer's number of children, and their ages, in other cases on the number of cars the family has, etc. So, as the agent cannot introduce a full questionnaire to the customer, there is a great need to obtain in real time a minimal, preferably one most vital parameter. But how can the agent determine in real time what is this missing most vital parameter? Moreover, how can the agent determine the missing parameter when this parameter depends on real time data relating to the present call (for example to the type of product A)? The present invention provides a tool for overcoming this real time complicated challenge.
Further, most typical CRM systems record only the transactions that were approved by the customer and carried out. In other words, when a customer refuses a suggested offer, the information regarding the refused offer is typically not recorded. This has two negative impacts:    1. The next time the customer calls, the same offer might be suggested to him and refused.    2. The data mining engine cannot use this additional information, i.e., the determination that this offer has already been turned down by “this type of customers”, in order to improve its overall predictions.
By using the real time capabilities of the present invention, this refusal information is also recorded and stored for future analysis.
Furthermore, by providing the real time system of the invention, the determination of accurate situation identification and interaction control, such as, when during the call would it be most meaningful to provide an offer, becomes feasible.
It is therefore an object of the present invention to provide a mechanism for increasing the revenue per service or product agent interaction in a call center.
It is another object of the present invention to provide a real-time analytical means for improving the revenue at the whole call center.
It is also an object of the present invention to provide means for optimizing the gathering of information by the agents of a call center.
It is still another object of the present invention to provide means for closing the gap between the analytical tools (such as data mining engines) that are used today off-line, and the agent behavior in call centers, and to allow using insights about real-time data gathered during the present call in order to increase revenues.
It is still another object of the present invention to provide means for improving sale tactics and sale strategies in the call center.
It is still another object of the present invention to provide means for quick experimentation of business hypothesis within the call center, and validating the results of these experiments with analytical tools.
It is still another object of the present invention to provide a tool for quick shortcut construction in existing CRM processes, to ensure the information completion value of customer interactions. This is designed to overcome the CRM rigid process definition, which does not allow for shortcuts.
It is still another object of the present invention to provide means for dynamically ranking information collection priorities according to their value to the company, as derived by analytical tools.
It is still another object of the present invention to provide means for gathering the vital information from the customer independently of the conventional main operational system of the company, thus eliminating the risk of damaging the operational system performance, structure or access.